In the dynamic landscape of smart grid technology, the application of machine learning (ML) techniques has become instrumental for optimizing energy management and refining consumer categorization. This research explores time-based categorization within the smart grid paradigm, utilizing a Random Forest (RF) classifier to discern patterns among 29,546 consumers. The dataset, capturing hourly frequency data over 24 h, undergoes meticulous preparation, including standard scaling for robust model training. Noteworthy is the comparison of scaled data outcomes with an existing article, revealing insights into the impact of scaling techniques on classifier performance. Additionally, Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) contribute as novel dimensions, addressing the curse of dimensionality. The reduced-dimensional representations from PCA and LDA enhance the RF Classifier's performance, resulting in a nuanced comparative analysis against the original scaled data. Crucially, the study highlights that employing original scaled data in the RF classifier yields the best result, achieving a remarkable 99% accuracy and surpassing the outcomes of previous research. This finding underscores the pivotal role of standard scaling in optimizing classifier performance. The research not only advances our understanding of ML applications in smart grids but also provides a comprehensive framework for refining classifier performance and driving progress in energy management strategies.